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true and false in the ar1_xam
input arguments
and
output
were changed to
yes and no respectively.
This was also done for the capture_xam
program.
bin/speed_compare.py program
(which has since been moved to bin/speed_old.py).
capture_xam be more similar to
ar1_xam
.
cppad_mixed with the
checkpoint_newton_step option.
Previously, the Newton step was always checkpointed.
initialize Syntax. This has been fixed.
make speed command; see
speed
.
sparse_ad_cholesky; to be more specific,
the inverse permutation @(@
P^{-1}
@)@ was being used where the permutation
P
in sparse_ad_cholesky was required.
This the first version with cppad_mixed passes all its tests with
use_atomic_cholesky
true.
cmake command line.
use_atomic_cholesky
option to the cmake command line.
sparse_ad_cholesky. To be specific,
when search for the beginning of a row, do not check that at column index
is less than an element known to be in the row before you get to the row.
install_suitesparse.sh
(introduced on 2016-10-24).
sparse_ad_cholesky class forward mode.
To be specific, ignore higher order Taylor coefficients that do
not appear in the sparsity patter for the zero order coefficient
(they are no effects).
log_fatal_error option (which has since been removed).
log_fatal_error option to the cppad_mixed configuration.
A_rcv
, is now specified in
initialize
instead
derived_ctor
.
This was undone on
2017-03-08
.
metis package.
Note that the proper value for
metis_version
,
in the suitesparse install,
is printed at the end of the ipopt install.
sparse_ad_cholesky.
libdir
for all the install scripts;
see cmake_libdir
.
cppad_mixed).
This enables one to build into the actual prefix
(without debug or release at the end)
by not ending the prefix with cppad_mixed.
sparse_up_tri_sol
(plan to use this with a sparse atomic Cholesky AD operation).
sparse_low_tri_sol
(plan to use this with a sparse atomic Cholesky AD operation).
install_suitesparse.sh
so they better ensure that debug and release versions got into proper
directories.
start_near_solution
argument to
ar1_xam.cpp
and capture_xam.cpp
.
quasi_fixed
argument to ar1_xam.cpp
and change its position in capture_xam.cpp
(so it has same relative location as in ar1_xam.cpp).
2 and the default for
obj_scaling_factor
back to 1.
obj_scaling_factor
from 1.0 to
obj_scaling_factor = 1.0 / sqrt( n_fixed + n_random )
method
in ldlt_cholmod_inv.
The
method
in ldlt_eigen_inv has not been changed.
number_times
to
number_random
(in both programs).
In addition, the
derivative_test
argument was added to both programs.
hold_memory
parameter to
capture_xam.cpp
and
ar1_xam.cpp
.
bool_sparsity
parameter to the following:
initialize
,
information_mat
,
capture_xam.cpp
,
ar1_xam.cpp
.
In addition, remove
bool_sparsity
from the run_cmake.sh
configuration script.
This puts the choose between boolean and set sparsity patterns
during run-time, instead of during the install
process.
The
bool_sparsity
parameter has since been moved for
initialize and information_mat to
derived_ctor
.
ipopt_solve
in
capture_xam
and
ar1_xam
.
src/cholmod/sim_cov.cpp.
ok
flag; e.g., see
ok in ipopt_xam_eval_f.
capture_xam.cpp.
auto_regressive_xam.cpp example to the
ar1_xam.cpp
example and speed test.
bin/debian_install has been moved to
bin/example_install.sh
and it should work for both Debian and Red Hat systems.
auto_regressive_xam.cpp example.
bin/debian_install.sh for an easy install on Debian
systems. There are some 2DO items in this install that need to be
fixed so they work on all systems.
bin/install_omhelp an easy install of omhelp
for Debian systems. This is needed to do cppad_mixed development.
build_type
choice
(debug or release) to the install script for
eigen
,
ipopt
, and
suitesparse
.
Compiling debug versus release
for the installs above and for the
build_type
made a difference of 40.0 seconds versus 5.5 seconds
for the average of 20 values of capture_xam
optimize_fixed_seconds
.
solution.fixed_opt
a1_double version of the user defined
ran_likelihood
function.
Also drop the corresponding template functions from the
user_examples
.
Assertion `H_info.col[k] == j' failed.
The problem was that H_info was not in
column manor order
.
This has been fixed.
box_newton to optimize the random effects
(added on 06-03
).
It turns out this had some problems and did not yield the hoped for
improvements in speed.
box_newton fails, change message from
fatal_error
to
warning
.
box_newton convergence
tolerance to be in terms of the
Newton step (instead of projected gradient).
box_newton line search termination criteria
(did not affect any test results).
box_newton more robust by doing line search in a
scaled negative projected gradient direction.
(Step size of one in negative projected gradient direction
may be much too large or much too small).
ldlt_cholmod_solve_H where an unnecessary
condition was check and failed. The example
cholmod_solve2_a.cpp has been change to demonstrate that
only a subset of the right hand side values matter.
ldlt_eigen_logdet function in the special case where the
determinant was zero. This has been fixed.
box_newton
to optimize the random effects.
This changed the user API for the functions listed below
(but has since been remove and the API has been changed back):
options
argument determines
if ipopt or box_newton is used.
random_options
have been split into
random_box_options/
and
random_ipopt_options
.
random_options
have been split into
random_box_options/
and
random_ipopt_options
.
sample_fixed results
(with a warning) when the information matrix is invertible,
but not positive definite.
This is intended to help determine which variables are
not determined by the model plus data.
sample_fixed wish list item.
adaptive and trace-adaptive choices to the
derivative_test
fixed effects options.
Note that this gives the user direct control over doing this test
(which can be time intensive for large problems).
sample_fixed wish list item.
cholmod class to ldlt_cholmod class
and choleig class to ldlt_eigen class.
cholmod_cholesky to
ldlt_cholmod
in run_cmake.sh.
ldlt_cholmod to be NO.
This has been fixed by changing the ldlt_eigen_solve_H
function to work the same as the ldlt_cholmod_solve_H function.
cholmod_solve2_sim.cpp, an example that shows
how to use Cholmod to efficiently sample a distribution that
has covariance equal to the inverse of a sparse positive definite matrix.
cppad_mixed
when checking if a fixed effect
bound
or a fixed constraint
bound
is active. This should give better detection.
optimize_fixed would have non-zero values for
fixed_lag
that should have been zero.
This has been fixed.
bin/install_cppad.sh).
This fixed the remaining warnings when building a release version.
RELEASE.
ipopt_fixed_adapt_derivative_chk now checks
ipopt_fixed_eval_jac_g in addition to
ipopt_fixed_eval_grad_f.
ipopt_fixed_adapt_derivative_chk fails,
it now results in warning instead of a fatal error.
-1.
ipopt_fixed_adapt_derivative_chk to handle case where
the function at both central difference points is zero,
and the derivatives is also zero.
ipopt_fixed_adapt_derivative_chk to down to step size
1e-9 (used to just go to 1e-7.
In addition, in error report changed step= to best_step=.
cppad_mixed defined routines,
and other examples.
optimize_fixed. This has been fixed.
fixed_out
was the return value for the previous version of optimize_fixed,
and
solution
is the current return value,
fixed_out = solution.fixed_opt
optimize_fixed calls to the new form.
ran_likelihood_hes function
(since removed).
ran_likelihood_jac function
(since removed).
ldlt_cholmod_solve_H.
to be specific, accessing past the end of the
row
vector in ldlt_cholmod_solve_H, in addition it must be
in increasing order (and was not specified as such).
A_rcv
to be an argument to the
derived class constructor
instead of an argument to the
initialize
routine.
This was undone on
2016-10-15
and redone on
2017-03-08
.
cholmod Cholesky factorization package.
ldlt_cholmod wish list its (completed) and add
partial Newton step second order method wish list item
(this item has been completed).
set_sparsity to bool_sparsity
(so that the default value is YES).
cholmod for sparse Cholesky factorization;
see ldlt_cholmod
.
cppad_mixed_eigen
library. In addition, the new example check_install.sh
demonstrates compiling, linking and running a program using
cppad_mixed.
bin/install_eigen.sh places
the Eigen package (because warnings are suppressed for this directory).
valgrind) in the setting of the upper bound
(
g_u
in ipopt_fixed_get_bounds_info) for the
random constraints
.
assert(false)).
Furthermore, change the tolerance in one of the solution tests
to avoid a warning (when convergence has been achieved).
ldlt_eigen Cholesky factorization of the Hessian
with respect to the random effects has been changed from a static
variable to the private member variable
ldlt_ran_hes_.
This has many advantages; e.g.,
it allows for multiple derived_ctor
objects.
-Wno-shadow and -Wno-deprecated-declarations options
when compiling Eigen files by using the CMake system option
for the
Eigen
include directories.
This should work on more systems.
bin/install_cppad.sh).
dismod_at to
cppad_mixed.
This should have been done when cppad_mixed was extracted
from dismod_at.